Session1 04.florian fink
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The document presents the a-i-pocoto system, which integrates automated and interactive OCR post-correction methods for improving the accuracy of OCR results on historical documents. It details the process of automatic post-correction using supervised machine learning, involving multiple OCRs and profiling for error detection, and describes an interactive tool known as Pocoto for manual corrections. Evaluation results indicate improvements in OCR word accuracy through various experimental setups, though certain steps like lexicon extension showed limited benefits and suggested training adjustments for improving decision-making in corrections.
![A-PoCoTo — Ranking step
In the Ranking step the profiler’s candidates are re-ranked.
Classification starts after the second profiling round
All whist,cand for each wocr are considered
Set of features for each whist,cand (token-shape, candidate unigram
frequencies, agreeing OCRs, . . . )
Classifier classifies whist,cand as True or False
Candidates are re-ranked using the classifier’s confidence values
([−1, 1])
Florian Fink (CIS) A-I-PoCoTo 9. May 2019 10 / 16](https://image.slidesharecdn.com/session1-04-190926115722/85/Session1-04-florian-fink-10-320.jpg)
Session1 04.florian fink
- 1. A-I-PoCoTo — Combining Automated and Interactive OCR Postcorrection Tobias Englmeier, Florian Fink and Klaus U. Schulz 9. May 2019 Florian Fink (CIS) A-I-PoCoTo 9. May 2019 1 / 16
- 2. Overview Automatic post-correction (A-PoCoTo) Evaluation results Automatic interactive post-correction (A-I-PoCoTo) Resume Florian Fink (CIS) A-I-PoCoTo 9. May 2019 2 / 16
- 3. A-PoCoTo Automatic post-correction of OCR-results of historical documents using supervised machine learning. Multiple OCRs (OCR1, OCR2, . . . , OCRn) can be used 3 steps with two profiling rounds 3 classifiers for 1, 2, . . . , n OCRs Classifiers are trained using logistic regression Developed as a module of the OCR-D project 1 1 http://www.ocr-d.de/ Florian Fink (CIS) A-I-PoCoTo 9. May 2019 3 / 16
- 4. PoCoTo PoCoTo (Post-Correction Tool) is a tool for manual interactive post-correction of OCRed historical Documents. Initially a desktop application (2014)2 New version as web-application (2017) Profiling used for error detection and correction suggestions Batch correction of (error-) patterns 2 Vobl, T., Gotscharek, A., Reffle, U., Ringlstetter, C., & Schulz, K. U. (2014, May). PoCoTo-an open source system for efficient interactive postcorrection of OCRed historical texts. In Proceedings of the First International Conference on Digital Access to Textual Cultural Heritage (pp. 57-61). ACM. Florian Fink (CIS) A-I-PoCoTo 9. May 2019 4 / 16
- 5. Profile (global) Given an OCRed historical text, the profiling derives a ‘statistical picture’ (guess) of the language in the document using various background lexica3 OCR errors and OCR error series Historical patterns of the form mod → hist (t → th, ei → ey, ...) Underlying modern words The profile is used as a feature generator for the automatic post-correction system 3 Reffle, U., & Ringlstetter, C. (2013). Unsupervised profiling of OCRed historical documents. Pattern Recognition, 46(5), 1346-1357. Florian Fink (CIS) A-I-PoCoTo 9. May 2019 5 / 16
- 6. Profile (local) Profiling associates with each token wocr of a document a set of interpretations wmod,cand →α whist,cand →β wocr is generated. α- (historical patterns) and β- (OCR-errors) channels can be empty Interpretations have a weight Each wocr has a ranked set of interpretations wcand,hist Florian Fink (CIS) A-I-PoCoTo 9. May 2019 6 / 16
- 7. A-PoCoTo Alignment Profiling Lexicon Extension Profiling Ranking Decision A-I-PoCoTo OCR1 OCR2 OCRn Florian Fink (CIS) A-I-PoCoTo 9. May 2019 7 / 16
- 8. Multiple OCRs One master-OCR Additional support-OCRs (optional) OCRs are token-wise aligned with the master-OCR Each wocr has n − 1 additional OCR-tokens wocr2 , wocr3 , . . . , wocrn Florian Fink (CIS) A-I-PoCoTo 9. May 2019 8 / 16
- 9. A-PoCoTo — Lexicon Extension step In the lexicon extension step a classifier tries to find good wocr to extend the profiler’s back-end resources. Classification starts after the first profiling round wocr with a non empty α or β channel are considered Set of features for each wocr (token-shape, candidate set, unigram frequencies, agreeing OCRs, . . . ) Classify wocr as True or False True tokens are put into the extended lexicon for the second profiler round Florian Fink (CIS) A-I-PoCoTo 9. May 2019 9 / 16
- 10. A-PoCoTo — Ranking step In the Ranking step the profiler’s candidates are re-ranked. Classification starts after the second profiling round All whist,cand for each wocr are considered Set of features for each whist,cand (token-shape, candidate unigram frequencies, agreeing OCRs, . . . ) Classifier classifies whist,cand as True or False Candidates are re-ranked using the classifier’s confidence values ([−1, 1]) Florian Fink (CIS) A-I-PoCoTo 9. May 2019 10 / 16
- 11. A-PoCoTo — Decision step In the Decision step a classifier decides if the best ranked candidate for any wocr should be used as a correction for wocr . Re-ranked candidate set for each wocr are considered Confidence for highest candidate and distance to next candidate are the features Classifier classifies highest ranked candidate as True or False True candidates are used to correct the corresponding wocr Florian Fink (CIS) A-I-PoCoTo 9. May 2019 11 / 16
- 12. A-PoCoTo — Evaluation results Post-correction model trained on OCR-D4 ground truth Documents from 16th to 19th century 574 pages from 90 documents (3-6 pages per doc.) Profile for each document separately Evaluated two documents: ‘1557, Bodenstein, WieSichMeniglich’ (20 pages) ‘1841, Die Grenzboten’ (50 pages) Four experiments: 1LE (Only master OCR) 1noLE (Only master OCR, LE step omitted) 2LE (One additional support OCR) 2noLE (One additional support OCR, LE step omitted) 4 http://www.ocr-d.de/gt Florian Fink (CIS) A-I-PoCoTo 9. May 2019 12 / 16
- 13. A-PoCoTo — Evaluation results 2noLE provided best improvement of accuracy: ‘1557, Bodenstein, WieSichMeniglich’: OCR word accuracy: 65,63% → 69,81% ‘1841, Die Grenzboten’: OCR word accuracy: 77,57% → 80,63% Lexicon Extension does not offer benefit Ranking step help finding the best correction candidate Support OCR’s offer improvements (if not combined with LE step) Too many lost chances in both documents → Decision-Step too hesitant with corrections Florian Fink (CIS) A-I-PoCoTo 9. May 2019 13 / 16
- 14. A-I-PoCoTo Combine the automatic post-correction with the interactive post-correction of PoCoTo (work in progress). Users review and approve (reject) the additional lexicon entries of the extended lexicon. Users can inspect all correction decisions carried out (or not carried out) and revert (or apply) them The trained base models for the automatic post-correction can be further improved with the manually corrected document Florian Fink (CIS) A-I-PoCoTo 9. May 2019 14 / 16
- 15. Resume Automatic post-correction can improve accuracy Lexicon Extension step does not help → leave out or use only after manual inspection Feature-based re-ranking step improves the ranking of the profiler Automatic post-correction is too cautious → change training of Decision step to make it more courageous Automatic post-correction can support the interactive post-correction General problem: different alphabets between OCR-engines, Profiler (and ground-truth) Florian Fink (CIS) A-I-PoCoTo 9. May 2019 15 / 16
- 16. A-I-PoCoTo — Combining Automated and Interactive OCR Postcorrection Tobias Englmeier, Florian Fink and Klaus U. Schulz 9. May 2019 Florian Fink (CIS) A-I-PoCoTo 9. May 2019 16 / 16